{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b710d3c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEFCAYAAADt1CyEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVvUlEQVR4nO3de3CU5aHH8d8uSUQuiiSbxNF6EHQGx5arrSzUQJAEiEAuhIqpyKVWsBAgZKQppqaVS6mRpjA9nCojCsJByJhQOhXlkoowRK2cKdvaQqk0TLCpxCAlGEg22ff8wXQlhc0udd/s8vD9zDiTvbzv88vu8svjs+++67AsyxIAwFjOSAcAANiLogcAw1H0AGA4ih4ADEfRA4DhKHoAMBxFDwCGi4l0gEA+++xz+Xwc4g8AoXA6Hbrllu5XvC1qi97nsyh6AAgDlm4AwHAUPQAYjqIHAMNR9ABgOFuLvqqqSjk5ORo/fryWLVtm51AAgABsK/ra2lqVlJRo7dq12rFjh/70pz9p3759dg0HAAjAtsMrd+/erYyMDCUnJ0uSysrKdMMNN9g1HAAgANuK/sSJE4qNjdWcOXNUV1enUaNGaeHChSFvHx/fw65ouEZYliWHw3HdZ0D0aWtuVpcIT1yvJoNtRd/W1qYPPvhAr776qrp166Ynn3xSlZWVysnJCWn7hoZzfGDqOudy9dTHDWcjmuG2+JtUX98Y0QyIPi5XT20amhLRDI8eeqfda9PpdAScINu2Rp+QkCC3263evXura9euGjNmjDwej13DAQACsK3oU1NTdeDAAZ09e1ZtbW3av3+/7r33XruGAwAEYNvSzcCBA/X4448rLy9PXq9XI0aM0OTJk+0aDgAQgK0nNcvNzVVubq6dQwAAguCTsQBgOIoeAAxH0QOA4Sh6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDUfQAYDiKHgAMR9EDgOEoegAwHEUPAIaj6AHAcBQ9ABiOogcAw1H0AGA4ih4ADEfRA4DhKHoAMBxFDwCGo+gBwHAUPQAYjqIHAMNR9ABguBg7dz5t2jSdPn1aMTEXh3n22Wc1cOBAO4cEAPwb24resizV1NTot7/9rb/oAQCdz7alm+PHj0uSZs2apUmTJmnTpk12DQUA6IBtU+2zZ8/K7Xbrhz/8obxerx577DHdeeedGjFihF1DAgCuwLaiHzx4sAYPHuy/nJubq3379oVc9PHxPeyKZhtva5tiY7pc9xlM43L1jHSE64bP65UzNjbSMaImRzChvjZtK/oPPvhAXq9Xbrdb0sU1+6tZq29oOCefz7Irni1crp6a9eLuiGZY/0Sa6usbI5ohXKKlYE15PK8FLldPfbDkyUjH0H0r/qfD5z0aX5tOpyPgBNm2NfrGxkY999xzam5u1rlz51RZWam0tDS7hgMABGDbjD41NVWHDx9WVlaWfD6f8vLy2i3lAAA6h63HPS5cuFALFy60cwgAQBB8MhYADEfRA4DhKHoAMBxFDwCGo+gBwHAUPQAYjqIHAMNR9ABgOIoeAAxH0QOA4Sh6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDUfQAYDiKHgAMR9EDgOEoegAwHEUPAIaj6AHAcBQ9ABiOogcAw1H0AGA4ih4ADEfRA4DhbC/6n/70pyoqKrJ7GABAALYWfXV1tSorK+0cAgAQhG1Ff+bMGZWVlWnOnDl2DQEACEGMXTt+5plnVFBQoLq6uv9o+/j4HmFOdP1wuXpGOoJRTHg8rbY2Obp0iXSMqMkRimvheQ81oy1FX15erltvvVVut1sVFRX/0T4aGs7J57PCnMxe0fLCqK9vjHSEsODxDB+Xq6c+3fO/kY6hhDF5HT6e0fKcSx0/79GS89KMTqcj4ATZlqJ/4403VF9fr8zMTP3zn/9UU1OTVqxYoSVLltgxHACgA7YU/csvv+z/uaKiQu+//z4lDwARwnH0AGA4296M/ZecnBzl5OTYPQwAIABm9ABgOIoeAAxH0QOA4Sh6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDUfQAYDiKHgAMR9EDgOGCFv2nn36qvXv3SpJKS0s1ffp0HTlyxPZgAIDwCFr0RUVFqq2tVXV1tfbv36/MzEwtW7asM7IBAMIgaNGfOXNGM2bM0DvvvKMJEyYoJydH58+f74xsAIAwCFr0Xq9XXq9X+/fv1/Dhw3X+/Hk1NTV1RjYAQBgELfoHH3xQbrdbt9xyi7761a9qypQpmjBhQmdkAwCEQdAvHpk/f76+9a1vKSkpSZL0/PPPq3///rYHAwCER8Ci/9WvfqXMzMx23//6L9XV1Zo5c6atwQAA4RGw6E+cOCFJ+stf/tJpYQAA4Rew6OfPny9Jeuqpp9S7d+92t3EcPQBcO4K+GZuTk6NDhw75L2/cuFEzZsywMxMAIIyCvhm7YsUKLVq0SFOnTtXhw4fV2Nio8vLyzsgGAAiDoEU/fPhwPfPMM5o3b54SEhL0+uuvKzExsTOyAQDCIOjSTWlpqUpKSrR27VpNmzZNkydP1u7duzsjGwAgDILO6D/88ENVVlbK5XIpNTVVw4YNU2FhodLS0jojHwDgSwpa9C+//LIcDof/8oABA1RZWRnSzlevXq233npLDodDubm5HHsPABEQtOh///vf68UXX1RTU5Msy5LP59PJkyf19ttvd7jd+++/r3fffVc7duxQa2urMjIyNHLkSPXt2zdc2QEAIQi6Rl9cXKzBgwfr3Llzmjhxonr06KH09PSgO/7GN76hjRs3KiYmRg0NDWpra1O3bt3CEhoAELqgM3qHw6EnnnhCn332mfr27auJEydq8uTJIe08NjZWa9as0fr16zVu3Dj/+XIAAJ0naNF3795dknTHHXfo2LFjGjp0qJzO0L+BcP78+frud7+rOXPmaNu2bXr44YdD2i4+vkfIY6A9l6tnh7e3+XzqchXPoR2iIUOogj2euDrXyuN5LeQMNWPQoh8wYIAWLlyoBQsWaPbs2aqpqVFMTNDN9NFHH6mlpUX33HOPbrzxRqWnp+vo0aMhhZKkhoZz8vmskO8fDaLlhVFf39jh7S5XT23/v486Kc2VZQ3pF1LOaBAs57UgWh5LqePHk5xX59KMTqcj4AQ56JRqyZIlmjFjhu688049/fTT8vl8WrVqVdAAJ0+eVHFxsVpaWtTS0qK9e/dq6NChV/ErAADCIaQ1+kGDBkmSPB6PlixZEtKOR44cKY/Ho6ysLHXp0kXp6el66KGHvlRYAMDVC74Gc4mqqirl5+eHfP/8/Pyruj8AIPyu6t0wy7q21swBACEUfX5+vg4ePChJGj16tO2BAADhFbTo09LStHbtWo0dO1bdu3fXmTNnOiEWACBcghb9pEmTtGnTJq1du1YNDQ3Kzc3VU089JY/H0xn5AABfUkhr9D6fTydOnFBNTY3a2toUHx+vH/3oR1qzZo3d+QAAX1LQo27KyspUUVGhr3zlK8rLy9Pq1asVGxurpqYmpaam+r9bFgAQnYIW/enTp7Vu3Tr179+/3fXdunUL6YNTAIDIClr0S5cuDXjbN7/5zbCGAQCE37VxVikAwH+MogcAw1H0AGA4ih4ADEfRA4DhKHoAMBxFDwCGo+gBwHAUPQAYjqIHAMNR9ABgOIoeAAxH0QOA4Sh6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDBf0qwS/jF7/4hXbu3ClJGjlypBYvXmzncACAK7BtRn/w4EEdOHBAlZWV2r59uz788EPt3r3bruEAAAHYNqN3uVwqKipSXFycJKlfv376+9//btdwAIAAbCv6u+++2/9zTU2Ndu7cqS1btoS8fXx8DztiXRdcrp6RjhASE3JaliWHw9GJaaI7RyhMeN6jRagZbV2jl6Rjx45p9uzZWrx4sfr06RPydg0N5+TzWfYFs0G0vDDq6xs7vJ2cV6ejnC5XT1345EQnprmyrkn/FTRntCBn+Fya0el0BJwg23rUzaFDhzRjxgwVFhYqOzvbzqEAAAHYNqOvq6vT3LlzVVZWJrfbbdcwAIAgbCv6l156Sc3NzVq5cqX/uqlTp+qRRx6xa0gAwBXYVvTFxcUqLi62a/cAgBDxyVgAMBxFDwCGo+gBwHAUPQAYjqIHAMNR9ABgOIoeAAxH0QOA4Sh6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDUfQAYDiKHgAMR9EDgOEoegAwHEUPAIaj6AHAcBQ9ABiOogcAw1H0AGA4ih4ADEfRA4DhKHoAMJytRX/u3DlNmDBBJ0+etHMYAEAHbCv6w4cP65FHHlFNTY1dQwAAQmBb0W/btk0lJSVKTEy0awgAQAhi7Nrx8uXL7do1AOAq2Fb0X1Z8fA//z83eVt0QG9mo0ZAhVC5Xz0hHCAk5w4uc4XUt5Aw1Y9Q2V0PDOfl8lqSLv8z9c9dHNM97/z1L9fWNHd4nWl4Y5AyvjnJGS0aJnOF2LeS8NKPT6Wg3Qb4Uh1cCgOEoegAwnO1LN1VVVXYPAQDoADN6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDUfQAYDiKHgAMR9EDgOEoegAwHEUPAIaj6AHAcBQ9ABiOogcAw1H0AGA4ih4ADEfRA4DhKHoAMBxFDwCGo+gBwHAUPQAYjqIHAMNR9ABgOIoeAAxH0QOA4Sh6ADAcRQ8AhrO16H/9618rIyND6enp2rx5s51DAQACiLFrx5988onKyspUUVGhuLg4TZ06Vffff7/uuusuu4YEAFyBbUV/8OBBDRs2TL169ZIkjR07Vm+++abmzZsX0vZOp6Pd5Vt79wh3xKv275muJL5H105I0rFQcnaLs+2pD1koObuEcB+7BcvpcHbppCQdC5bT2bV7JyXpWLCccb16d1KSjgXL2f3W5E5KEtilGTvK67Asy7IjwAsvvKCmpiYVFBRIksrLy+XxeLR06VI7hgMABGDbGr3P55PD8cVfGMuy2l0GAHQO24o+OTlZ9fX1/sv19fVKTEy0azgAQAC2Ff3w4cNVXV2t06dP6/z589q1a5dSUlLsGg4AEIBt78glJSWpoKBAjz32mLxer3JzczVgwAC7hgMABGDbm7EAgOjAJ2MBwHAUPQAYjqIHAMNR9ABgOIoeAAwX+ROe2OCjjz7SW2+9pX/84x9yOp1KTEzUAw88oK997WuRjnZN2rNnj+rq6jRy5Ejdcccd/uu3bt2qhx9+OILJ2qupqdGNN96opKQklZeX6+jRoxoyZIgyMjIiHS2glStXqqioKNIx2vF4PP5Doaurq7Vv3z7FxMQoLS1NAwcOjHC69vbv36+BAwfqpptu0vbt2+XxeHTvvfdq8uTJkY7mt2zZMuXn5+vmm2+OWAbjDq/cvHmztm3bprFjx8rlckm6+KncXbt2adKkSZo1a1aEE15bnn/+ef3xj39Uv3799Oabb2rx4sXKzMyUJGVnZ6uysjLCCS965ZVX9Oqrr8rn82nYsGGqq6tTWlqaqqqqNGTIEM2dOzfSEfWDH/zgsuuqqqo0evRoSdJPfvKTzo50Rf96Xjdv3qzXXnvNX5qVlZWaMmWKHn300QgnvGj58uX685//rLKyMm3evFkej0djxozRO++8o9tvv13FxcWRjihJuu+++xQfH6/CwkKlp6dHJoRlmPT0dKupqemy65uamqyxY8dGIFFgH3/8cYf/RYMJEyZYXq/XsizL+tvf/malpqZab7zxhmVZlpWZmRnBZO1NmDDBunDhgnXy5Elr0KBB1oULFyzLsqzm5mZr4sSJEU530cqVK61hw4ZZr7zyilVRUWFVVFRYo0aN8v8cLbKysizLsqxJkyZZp0+f9l/f2NgYVf+GMjIyrNbWVsuyLmZubm62LMuyWltbrXHjxkUyWjuZmZnWsWPHrG9/+9tWbm6u9Zvf/MY6f/58p2YwbukmJiZGra2tl11/4cIFxcbGRiBRYLNnz1ZNTY0SExNl/dv/WDkcDu3duzdCyb5gXXIyuj59+uiFF17QzJkz1bt376g6SZ3P51NcXJxuu+02zZo1SzfccIP/tra2tggm+8L3v/99paSk6Oc//7kWLVqk+++/Xxs2bFB2dnako7XT2toqn8+nXr16KS4uzn99XFycnM7oeVuva9euamhoUGJiopKTk9XU1KS4uDidP39eMTHRU20Oh0N33XWXNm3apIMHD2rr1q1avny5+vTpo+TkZK1atcr2DNHzaITJnDlzlJWVJbfbLZfLJYfDoVOnTundd9/1nzI5WmzZskV5eXkqKSnR0KFDIx3nisaNG6dp06apqKhIAwYM0N13363Vq1dr3rx5amlpiXQ8v/T0dD366KPauHGj8vPzJUlHjhxRcXGxxo8fH+F0X3C73brnnntUUlKit99+O2r+CF2qV69eGjVqlCRp6dKlWrlypaqrq1VaWqpx48ZFNtwl5s6dq9zcXD300EO6/fbbNW3aNLndbh04cECPP/54pOP5XTqJGz58uIYPHy6v16ujR4+qtra2UzIYt0YvXfx2q+rqap06dUo+n0/Jyclyu91KSkqKdLTLeDwelZeXR/V5+qurq5WYmKh+/fr5r6urq9P69ev19NNPRzBZe7/73e/09a9/3X/5+PHjqq2t1ciRIyOYKrDy8nLt3LlT69evj3SUKzp+/LjOnj2rQYMG6dChQ2psbPT/AYgWtbW12rNnj06cOKG2tjYlJCQoNTU1qs6rVV5erilTpkQ0g5FFDwD4QvQsuAEAbEHRA4DhKHogTFavXq3t27dHOgZwGdboAcBwzOhx3amsrNSYMWP0+eefq6mpSePHj79sJu7z+bRs2TJNmTJFGRkZGj9+vA4dOiSfz6fp06frueeekyQdPHhQKSkp+vTTT1VUVKSXXnpJkrRmzRpNnDhROTk5+s53vqNTp0519q8J+Bl3HD0QTHZ2tg4cOKDS0lK1tLTovvvuU1ZWVrv7HD58WKdOndLWrVvldDr14osvat26dfrlL3+p0tJSZWdna8iQIXr22We1atUqJSQk+Letq6vThg0bVF1drbi4OK1fv97/8XwgEih6XJd+/OMfKzMzU127dlVFRcVltw8ePFg333yzXnvtNdXW1uq9995T9+7dJUmJiYlaunSpvve97yk/P7/dsfvSxe9L7t+/v7Kzs5WSkqKUlBS53e5O+b2AK6HocV1qaGhQc3OzWlpadOrUKVVUVKiqqkqSNHr0aA0cOFDLly/XzJkz9eCDD6pv377asWOHf/u//vWvSkhIkMfjuWzfTqdTmzZt0h/+8AdVV1drxYoVeuCBB7R48eJO+/2AS1H0uO54vV4tWrRICxYskM/nU0FBgbZs2aIFCxb477N8+XKlpqYqLy9PFy5c0Lp16/ynK/B4PNq4caNef/11FRQUaMOGDZo+fbp/2yNHjqiwsFDbtm3TgAEDlJCQwNE4iCiKHtedn/3sZ0pISPB/LH3Pnj0qKytrN+OeOnWqCgsLNXHiRLW2tmrEiBHatWuXGhsbtWjRIhUXFyspKUkrV67UlClT2i3f9O/fX+PHj9fkyZPVrVs3de3aNWpOmYvrE4dXAoDhOLwSAAxH0QOA4Sh6ADAcRQ8AhqPoAcBwFD0AGI6iBwDDUfQAYLj/BwB0WGpHEtwJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(8)\n",
    "y = np.array([1,5,3,6,2,4,5,6])\n",
    "\n",
    "df = pd.DataFrame({\"x-axis\": x,\"y-axis\": y})\n",
    "\n",
    "sns.barplot(\"x-axis\",\"y-axis\",palette=\"RdBu_r\",data=df)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39a261a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2056: FutureWarning: The `axis` variable is no longer used and will be removed. Instead, assign variables directly to `x` or `y`.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, integrate\n",
    "import matplotlib.pyplot as plt #导入\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)#导入seaborn包设定颜色\n",
    "np.random.seed(sum(map(ord, \"distributions\")))\n",
    "\n",
    "x = np.random.normal(size=100)\n",
    "sns.distplot(x,bins=20, kde=False, rug=True);#kde=False关闭核密度分布,rug表示在x轴上每个观测上生成的小细条（边际毛毯）\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
